Last updated: 2023-12-20
Checks: 5 2
Knit directory: ILD_ASE_Xenium/
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| absolute | relative |
|---|---|
| /home/hnatri/ILD_ASE_Xenium/ | . |
| /home/hnatri/ILD_ASE_Xenium/code/colors_themes.R | code/colors_themes.R |
| /home/hnatri/ILD_ASE_Xenium/code/plot_functions.R | code/plot_functions.R |
| /home/hnatri/ILD_ASE_Xenium/code/utilities.R | code/utilities.R |
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 83e2df3 | heinin | 2023-12-19 | Adding lineage level annotations |
| html | 83e2df3 | heinin | 2023-12-19 | Adding lineage level annotations |
| Rmd | b1f446f | heinin | 2023-12-15 | Selecting PCs. Added utilities. |
Cell type annotations for spatial data.
suppressPackageStartupMessages({library(cli)
library(Seurat)
library(SeuratObject)
library(SeuratDisk)
library(tidyverse)
library(tibble)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(workflowr)
library(googlesheets4)})
Loading Seurat v5 beta version
To maintain compatibility with previous workflows, new Seurat objects will use the previous object structure by default
To use new Seurat v5 assays: Please run: options(Seurat.object.assay.version = 'v5')
setwd("/home/hnatri/ILD_ASE_Xenium/")
set.seed(9999)
options(ggrepel.max.overlaps = Inf)
# Colors, themes, cell type markers, and plot functions
source("/home/hnatri/ILD_ASE_Xenium/code/colors_themes.R")
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Epithelial''.
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Immune''.
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Mesenchymal''.
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''Endothelial''.
✔ Reading from "ILD spatial ASE cell type annotations".
✔ Range ''All celltypes, annotated, merged''.
source("/home/hnatri/ILD_ASE_Xenium/code/plot_functions.R")
source("/home/hnatri/ILD_ASE_Xenium/code/utilities.R")
seurat_object <- readRDS("/tgen_labs/banovich/IPF/Spatial_ASE/ILD_ASE_Xenium_processed_npcs20.rds")
DefaultAssay(seurat_object)
[1] "RNA"
# Dimplot of clusters
DimPlot(seurat_object,
group.by = "leiden_res0.5",
cols = cluster_col,
reduction = "umap",
raster = T,
label = T) +
coord_fixed(ratio = 1) +
NoLegend() +
theme_minimal()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
| Version | Author | Date |
|---|---|---|
| 83e2df3 | heinin | 2023-12-19 |
# Cell type markers
DotPlot(seurat_object,
features = unique(c(epithelial_features,
immune_features,
endothelial_features,
mesenchymal_features)),
group.by = "leiden_res0.5",
cols = c("azure", "tomato3")) +
coord_flip() +
theme_minimal()
Warning: The following requested variables were not found (10 out of 126
shown): NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
epi_clusters <- c(9, 7, 8, 9)
imm_clusters <- c(3, 5, 6, 10)
endo_mesen_clusters <- c(1, 2, 4)
epithelial <- subset(seurat_object, subset = leiden_res0.5 %in% epi_clusters)
# Saving the object for clustering with scanpy
#saveRDS(epithelial, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial.rds")
#epithelial <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_clustered.rds")
# Reclustering using Seurat. The function is sourced from /code/utilities.R
epithelial_reclustered <- recluster(epithelial)
# PCs for UMAP: 13
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
#saveRDS(epithelial_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_reclustered.rds")
#epithelial_reclustered <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_reclustered.rds")
# Dimplot of clusters
DimPlot(epithelial_reclustered,
group.by = "snn_res.0.3",
cols = cluster_col,
reduction = "umap",
raster = T,
label = T) +
coord_fixed(ratio = 1) +
NoLegend() +
theme_minimal()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
# Cell type markers
DotPlot(epithelial_reclustered,
features = unique(c(epithelial_features,
immune_features,
endothelial_features,
mesenchymal_features)),
group.by = "snn_res.0.3",
cols = c("azure", "tomato3")) +
coord_flip() +
theme_minimal()
Warning: The following requested variables were not found (10 out of 126
shown): NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
FeaturePlot(epithelial_reclustered,
features = epithelial_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 38 shown):
NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
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FeaturePlot(epithelial_reclustered,
features = immune_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 35 shown):
PPARG, LTB, HLA-DRA, CXCR4, PTPRC, CD69, CD3G, TRAC, ITM2C, CCL5
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
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FeaturePlot(epithelial_reclustered,
features = endothelial_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 23 shown):
APLN, HEY1, BMPR2, EPAS1, PECAM1, APLNR, COL15A1, POSTN, ZEB1, HAS1
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
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FeaturePlot(epithelial_reclustered,
features = mesenchymal_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 30 shown):
PI16, ELN, FAP, AXL, COL1A1, COL1A2, COL3A1, DCN, FN1, HAS2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
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To disable this behavior set `raster=FALSE`
Some clusters are not epithelial:
* Cluster 3: endothelial
* Clusters 0, 4, 5, 6, 8, 9, 10: immune
epithelial_reclustered$lineage <- ifelse(epithelial_reclustered$snn_res.0.3 %in% c(1, 2), "Epithelial",
ifelse(epithelial_reclustered$snn_res.0.3 %in% c(3), "Endothelial",
ifelse(epithelial_reclustered$snn_res.0.3 %in% c(0, 4, 5, 6, 7, 8, 9, 10), "Immune", NA)))
#saveRDS(epithelial_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_reclustered.rds")
immune <- subset(seurat_object, subset = leiden_res0.5 %in% imm_clusters)
# Saving the object for clustering with scanpy
#saveRDS(epithelial, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial.rds")
#epithelial <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_clustered.rds")
# Reclustering using Seurat. The function is sourced from /code/utilities.R
immune_reclustered <- recluster(immune)
# PCs for UMAP: 9
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
#saveRDS(immune_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/immune_reclustered.rds")
#immune_reclustered <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/immune_reclustered.rds")
# Dimplot of clusters
DimPlot(immune_reclustered,
group.by = "snn_res.0.3",
cols = cluster_col,
reduction = "umap",
raster = T,
label = T) +
coord_fixed(ratio = 1) +
NoLegend() +
theme_minimal()
# Cell type markers
DotPlot(immune_reclustered,
features = unique(c(epithelial_features,
immune_features,
endothelial_features,
mesenchymal_features)),
group.by = "snn_res.0.3",
cols = c("azure", "tomato3")) +
coord_flip() +
theme_minimal()
Warning: The following requested variables were not found (10 out of 126
shown): NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
FeaturePlot(immune_reclustered,
features = epithelial_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 38 shown):
NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
FeaturePlot(immune_reclustered,
features = immune_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 35 shown):
PPARG, LTB, HLA-DRA, CXCR4, PTPRC, CD69, CD3G, TRAC, ITM2C, CCL5
FeaturePlot(immune_reclustered,
features = endothelial_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 23 shown):
APLN, HEY1, BMPR2, EPAS1, PECAM1, APLNR, COL15A1, POSTN, ZEB1, HAS1
FeaturePlot(immune_reclustered,
features = mesenchymal_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 30 shown):
PI16, ELN, FAP, AXL, COL1A1, COL1A2, COL3A1, DCN, FN1, HAS2
Some clusters are not immune: * Cluster 5: epithelial * Clusters 3, 4: endothelial * Clusters
immune_reclustered$lineage <- ifelse(immune_reclustered$snn_res.0.3 %in% c(5), "Epithelial",
ifelse(immune_reclustered$snn_res.0.3 %in% c(3, 4), "Endothelial",
ifelse(immune_reclustered$snn_res.0.3 %in% c(0, 1, 2, 6, 7, 8), "Immune", NA)))
#saveRDS(immune_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/immune_reclustered.rds")
endo_mesen <- subset(seurat_object, subset = leiden_res0.5 %in% endo_mesen_clusters)
# Saving the object for clustering with scanpy
#saveRDS(epithelial, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial.rds")
#epithelial <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_clustered.rds")
# Reclustering using Seurat. The function is sourced from /code/utilities.R
endo_mesen_reclustered <- recluster(endo_mesen)
# PCs for UMAP: 10
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
#saveRDS(endo_mesen_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/endo_mesen_reclustered.rds")
#endo_mesen_reclustered <- readRDS("/scratch/hnatri/ILD/ILD_spatial_ASE/endo_mesen_reclustered.rds")
# Dimplot of clusters
DimPlot(endo_mesen_reclustered,
group.by = "snn_res.0.3",
cols = cluster_col,
reduction = "umap",
raster = T,
label = T) +
coord_fixed(ratio = 1) +
NoLegend() +
theme_minimal()
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
# Cell type markers
DotPlot(endo_mesen_reclustered,
features = unique(c(epithelial_features,
immune_features,
endothelial_features,
mesenchymal_features)),
group.by = "snn_res.0.3",
cols = c("azure", "tomato3")) +
coord_flip() +
theme_minimal()
Warning: The following requested variables were not found (10 out of 126
shown): NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
FeaturePlot(endo_mesen_reclustered,
features = epithelial_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 38 shown):
NKX2-1, RTKN2, NAPSA, PGC, SFTPC, KRT14, KRT5, KRT6A, S100A2, KRT17
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`
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FeaturePlot(endo_mesen_reclustered,
features = immune_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 35 shown):
PPARG, LTB, HLA-DRA, CXCR4, PTPRC, CD69, CD3G, TRAC, ITM2C, CCL5
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FeaturePlot(endo_mesen_reclustered,
features = endothelial_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 23 shown):
APLN, HEY1, BMPR2, EPAS1, PECAM1, APLNR, COL15A1, POSTN, ZEB1, HAS1
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FeaturePlot(endo_mesen_reclustered,
features = mesenchymal_features,
ncol = 3,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
NoLegend() &
theme_minimal()
Warning: The following requested variables were not found (10 out of 30 shown):
PI16, ELN, FAP, AXL, COL1A1, COL1A2, COL3A1, DCN, FN1, HAS2
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Some clusters are not endothelial/mesenchymal:
* Cluster 0: epithelial
* Cluster 2, 4, 6, 7: immune
endo_mesen_reclustered$lineage <- ifelse(endo_mesen_reclustered$snn_res.0.3 %in% c(0), "Epithelial",
ifelse(endo_mesen_reclustered$snn_res.0.3 %in% c(2, 4, 6, 7), "Immune", "Endo_Mesen"))
#saveRDS(endo_mesen_reclustered, "/scratch/hnatri/ILD/ILD_spatial_ASE/endo_mesen_reclustered.rds")
unique(epithelial_reclustered$lineage)
[1] "Immune" "Endothelial" "Epithelial"
unique(immune_reclustered$lineage)
[1] "Immune" "Endothelial" "Epithelial"
unique(endo_mesen_reclustered$lineage)
[1] "Immune" "Endo_Mesen" "Epithelial"
epithelial_subsets <- list("epi1" = subset(epithelial_reclustered, subset = lineage == "Epithelial"),
"epi2" = subset(immune_reclustered, subset = lineage == "Epithelial"),
"epi3" = subset(endo_mesen_reclustered, subset = lineage == "Epithelial"))
immune_subsets <- list("imm1" = subset(epithelial_reclustered, subset = lineage == "Immune"),
"imm2" = subset(immune_reclustered, subset = lineage == "Immune"),
"imm3" = subset(endo_mesen_reclustered, subset = lineage == "Immune"))
endo_mesen_subsets <- list("em1" = subset(epithelial_reclustered, subset = lineage %in% c("Endo_Mesen", "Endothelial")),
"em2" = subset(immune_reclustered, subset = lineage %in% c("Endo_Mesen", "Endothelial")),
"em3" = subset(endo_mesen_reclustered, subset = lineage %in% c("Endo_Mesen", "Endothelial")))
epithelial_merged <- merge(x = epithelial_subsets[[1]],
y = epithelial_subsets[2:length(epithelial_subsets)])
immune_merged <- merge(x = immune_subsets[[1]],
y = immune_subsets[2:length(immune_subsets)])
endo_mesen_merged <- merge(x = endo_mesen_subsets[[1]],
y = endo_mesen_subsets[2:length(endo_mesen_subsets)])
# Saving
#saveRDS(epithelial_merged, "/scratch/hnatri/ILD/ILD_spatial_ASE/epithelial_merged.rds")
#saveRDS(immune_merged, "/scratch/hnatri/ILD/ILD_spatial_ASE/immune_merged.rds")
#saveRDS(endo_mesen_merged, "/scratch/hnatri/ILD/ILD_spatial_ASE/endo_mesen_merged.rds")
sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ComplexHeatmap_2.16.0 RColorBrewer_1.1-3 viridis_0.6.3
[4] viridisLite_0.4.2 ggthemes_5.0.0 googlesheets4_1.1.0
[7] workflowr_1.7.1 ggrepel_0.9.3 ggpubr_0.6.0
[10] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[13] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4
[16] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[19] tidyverse_2.0.0 SeuratDisk_0.0.0.9021 Seurat_4.9.9.9048
[22] SeuratObject_4.9.9.9084 sp_1.6-1 cli_3.6.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.20 splines_4.3.0 later_1.3.1
[4] cellranger_1.1.0 polyclip_1.10-4 fastDummies_1.6.3
[7] lifecycle_1.0.3 rstatix_0.7.2 doParallel_1.0.17
[10] rprojroot_2.0.3 globals_0.16.2 processx_3.8.1
[13] lattice_0.21-8 hdf5r_1.3.8 MASS_7.3-60
[16] backports_1.4.1 magrittr_2.0.3 plotly_4.10.2
[19] sass_0.4.6 rmarkdown_2.22 jquerylib_0.1.4
[22] yaml_2.3.7 httpuv_1.6.11 sctransform_0.3.5
[25] spam_2.9-1 spatstat.sparse_3.0-1 reticulate_1.29
[28] cowplot_1.1.1 pbapply_1.7-0 abind_1.4-5
[31] Rtsne_0.16 BiocGenerics_0.46.0 git2r_0.32.0
[34] circlize_0.4.15 S4Vectors_0.38.1 IRanges_2.34.0
[37] irlba_2.3.5.1 listenv_0.9.0 spatstat.utils_3.0-3
[40] goftest_1.2-3 RSpectra_0.16-1 spatstat.random_3.1-5
[43] fitdistrplus_1.1-11 parallelly_1.36.0 leiden_0.4.3
[46] codetools_0.2-19 shape_1.4.6 tidyselect_1.2.0
[49] farver_2.1.1 stats4_4.3.0 matrixStats_1.0.0
[52] spatstat.explore_3.2-1 googledrive_2.1.0 jsonlite_1.8.5
[55] GetoptLong_1.0.5 ellipsis_0.3.2 progressr_0.13.0
[58] iterators_1.0.14 ggridges_0.5.4 survival_3.5-5
[61] foreach_1.5.2 tools_4.3.0 ica_1.0-3
[64] Rcpp_1.0.10 glue_1.6.2 gridExtra_2.3
[67] xfun_0.39 withr_2.5.0 fastmap_1.1.1
[70] fansi_1.0.4 callr_3.7.3 digest_0.6.31
[73] timechange_0.2.0 R6_2.5.1 mime_0.12
[76] colorspace_2.1-0 scattermore_1.1 tensor_1.5
[79] spatstat.data_3.0-1 utf8_1.2.3 generics_0.1.3
[82] data.table_1.14.8 httr_1.4.6 htmlwidgets_1.6.2
[85] whisker_0.4.1 uwot_0.1.14 pkgconfig_2.0.3
[88] gtable_0.3.3 lmtest_0.9-40 htmltools_0.5.5
[91] carData_3.0-5 dotCall64_1.0-2 clue_0.3-64
[94] scales_1.2.1 png_0.1-8 knitr_1.43
[97] rstudioapi_0.14 rjson_0.2.21 tzdb_0.4.0
[100] reshape2_1.4.4 nlme_3.1-162 curl_5.0.0
[103] GlobalOptions_0.1.2 cachem_1.0.8 zoo_1.8-12
[106] KernSmooth_2.23-21 parallel_4.3.0 miniUI_0.1.1.1
[109] pillar_1.9.0 vctrs_0.6.2 RANN_2.6.1
[112] promises_1.2.0.1 car_3.1-2 xtable_1.8-4
[115] cluster_2.1.4 evaluate_0.21 compiler_4.3.0
[118] rlang_1.1.1 crayon_1.5.2 future.apply_1.11.0
[121] ggsignif_0.6.4 labeling_0.4.2 ps_1.7.5
[124] getPass_0.2-2 plyr_1.8.8 fs_1.6.2
[127] stringi_1.7.12 deldir_1.0-9 munsell_0.5.0
[130] lazyeval_0.2.2 spatstat.geom_3.2-1 Matrix_1.5-4.1
[133] RcppHNSW_0.4.1 hms_1.1.3 patchwork_1.1.2
[136] bit64_4.0.5 future_1.32.0 shiny_1.7.4
[139] highr_0.10 ROCR_1.0-11 gargle_1.4.0
[142] igraph_1.4.3 broom_1.0.4 bslib_0.4.2
[145] bit_4.0.5